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Data validation refers to the process of ensuring that data is accurate, complete, consistent, and in the correct format before it is processed or stored. It is a crucial step in data management and software development to prevent errors, maintain data integrity, and improve the reliability of systems and applications.

The primary goal of data validation is to identify and correct or reject invalid data. This can involve checking data against predefined rules, constraints, or specifications. Common validation checks include verifying data types (e.g., ensuring a number is entered where a number is expected), checking for missing values, confirming that values fall within acceptable ranges, and ensuring data conforms to specific formats like dates or email addresses.

Data validation can be implemented at various stages, such as at the point of data entry, during data transmission, or as part of a data processing pipeline. Automated validation processes are typically employed to handle large volumes of data efficiently. The consequences of poor data validation can be far-reaching, leading to incorrect analyses, flawed decision-making, system failures, and security vulnerabilities. Therefore, robust data validation mechanisms are essential for any system that relies on accurate and trustworthy data.